DOCSLIB.ORG

Methodology for the Inference of Gene Function from Phenotype Data Joao a Ascensao1,2†, Mary E Dolan1*†, David P Hill1 and Judith a Blake1

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Methodology for the Inference of Gene Function from Phenotype Data Joao a Ascensao1,2†, Mary E Dolan1*†, David P Hill1 and Judith a Blake1 Ascensao et al. BMC Bioinformatics (2014) 15:405 DOI 10.1186/s12859-014-0405-z METHODOLOGY ARTICLE Open Access Methodology for the inference of gene function from phenotype data Joao A Ascensao1,2†, Mary E Dolan1*†, David P Hill1 and Judith A Blake1 Abstract Background: Biomedical ontologies are increasingly instrumental in the advancement of biological research primarily through their use to efficiently consolidate large amounts of data into structured, accessible sets. However, ontology development and usage can be hampered by the segregation of knowledge by domain that occurs due to independent development and use of the ontologies. The ability to infer data associated with one ontology to data associated with another ontology would prove useful in expanding information content and scope. We here focus on relating two ontologies: the Gene Ontology (GO), which encodes canonical gene function, and the Mammalian Phenotype Ontology (MP), which describes non-canonical phenotypes, using statistical methods to suggest GO functional annotations from existing MP phenotype annotations. This work is in contrast to previous studies that have focused on inferring gene function from phenotype primarily through lexical or semantic similarity measures. Results: We have designed and tested a set of algorithms that represents a novel methodology to define rules for predicting gene function by examining the emergent structure and relationships between the gene functions and phenotypes rather than inspecting the terms semantically. The algorithms inspect relationships among multiple phenotype terms to deduce if there are cases where they all arise from a single gene function. We apply this methodology to data about genes in the laboratory mouse that are formally represented in the Mouse Genome Informatics (MGI) resource. From the data, 7444 rule instances were generated from five generalized rules, resulting in 4818 unique GO functional predictions for 1796 genes. Conclusions: We show that our method is capable of inferring high-quality functional annotations from curated phenotype data. As well as creating inferred annotations, our method has the potential to allow for the elucidation of unforeseen, biologically significant associations between gene function and phenotypes that would be overlooked by a semantics-based approach. Future work will include the implementation of the described algorithms for a variety of other model organism databases, taking full advantage of the abundance of available high quality curated data. Keywords: Gene ontology, Mammalian phenotype ontology, Function prediction, Ontology development Background biomedical literature, to create empirical connections be- Ahallmarkofmodernbiomedicalresearchisthegener- tween different aspects of biological data, that is to say, ation of increasingly large amounts of scientific data. Bio- annotations [2]; .e.g. biocurators annotate, or tag, biolo- medical ontologies have the potential to greatly accelerate gical entities (e.g. proteins, functional RNAs) with ontol- biomedical research by enhancing our ability to integrate ogy terms (capturing all relevant metadata as well). One of and access these data. A biomedical ontology is a resource the most widely used modern bio-ontologies is the Gene that represents a controlled set of terms for entities in a Ontology (GO), a resource that describes canonical gene particular biomedical domain and how those terms are functions in a computable species-independent manner so related to one another [1]. Biocurators are scientists who that they may be used for statistical analysis of gene sets review experimental data, primarily as reported in the or for comparative genomic analysis [3,4]. Another bio- ontology is the Mammalian Phenotype Ontology (MP) that * Correspondence: [email protected] provides an independently curated set of terms and rela- † Equal contributors tionships describing non-canonical phenotypes, primarily 1The Jackson Laboratory, 600 Main Street, Bar Harbor, ME, USA Full list of author information is available at the end of the article © 2014 Ascensao et al.; licensee BioMed Central. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Ascensao et al. BMC Bioinformatics (2014) 15:405 Page 2 of 15 in mouse models, and that is used to query the effects of ‘guilt-by-association’ methods: annotation co-occurrence genetic mutations [5]. pairs, knowledge-based annotation inference based on, Genes and alleles (genetic variants) of genes are anno- for example, protein-protein interactions or pathway tated respectively for both function and phenotype within term enrichment. Mouse Genome Informatics (MGI) system, a compre- Our approach is strictly empirical and makes no as- hensive resource for genomic research of the laboratory sumptions about lexical matching, semantics, or ontology mouse [6]. Within the MGI curation workflow, different structure, except to infer annotations according to the subsets of biocurators separately process papers identified true-path rule. The rationale behind our approach is to for function (GO) and phenotype (MP) curation. As a re- make a simplifying assumption that in some cases ‘inter- sult, although papers may be selected for curation in esting’ biology could be missed by limiting the analysis to regards to both GO and MP, they are not processed simul- include an alignment of ontology structure or by attempt- taneously, leading to short-term temporal discrepancies in ing to compare the ‘meaning’ of phenotype versus GO overall curation coverage in MGI. Because scientific litera- terms. Using this simplified approach there is no under- ture is published much faster than biocurators can read lying assumption that ‘similar’ areas of the MPO and the and curate the papers, the development of methods to GO should correlate. Instead, we examine the feasibility of computationally infer annotations from one source to constructing rules based only on conjunctions and dis- another would greatly add and enhance curation effi- junctions of high-quality phenotype annotations made by ciency [7]. MGI curators to predict GO annotations. A crucial dif- Recent years have seen efforts to complement curated ference in our approach is that, where most empirical annotation data sets with text mined and association- methods group annotations based on gene entities, our rule mined predicted annotations. Broadly, text mining analysis is allele-specific, and therefore addresses the po- approaches use natural language processing methods to tential that a given set of varied phenotypes may be the alleviate the backlog of papers awaiting curation, while result of a single underlying genetic perturbation. Addi- association-rule mining uses curated annotation sets to tionally, mouse phenotypes can vary widely for different predict new annotations and to assess the validity of alleles of the same gene on different strain backgrounds. automated annotation methods [8-17]. It is worth noting Indeed, this is the reason for the detailed study of spon- that several of these same approaches have been used to taneous and targeted mutations in specific strains of the improve and expand the ontology structure and relation- laboratory mouse. Consider, for example, the phenotypes ships as well [18-21]. These efforts have been used both of three alleles of the mouse Pax3 gene. One spontaneous to predict additional annotations from curated annotations allele, Pax3Sp-d manifests phenotypes in many areas: em- in the same ontology and to predict across ontologies, bryogenesis, integument, limbs/digits/tail, mortality/aging, as we do here. nervous system, pigmentation. This allele is present in The prediction efforts mentioned above may include themousemodelforthehumandiseaseWaardenburg lexical matching [8], semantic similarity measures [9-11], Syndrome, Type 1; WS1 (OMIM:193500) [22,23]. An- ontology matching [18,19], and so-called ‘guilt-by-associ- other targeted allele, Pax3tm1Mrc manifests a different ation’ methods [12]; several efforts use a combination of set of phenotypes: craniofacial, growth/size, mortality/ these approaches [13]. Some prediction methods are pri- aging, muscle, nervous system, respiratory, skeleton, marily ontology based and others are annotation, or in- tumorigenesis, vision/eye. This allele is present in the stance, based. Our method uses an extension of ‘guilt- mouse model for another human disease Rhabdomyosar- by-association’ and is annotation based. coma 2; RMS2 (OMIM:268220) [24]. Yet another targeted Lexical matching methods, including text mining and allele, Pax3tm2.1Joe [25], is present in a mouse in which no text clustering, have been used to infer gene function abnormal phenotype is observed. from phenotype and vice versa. Semantic matching is In this work, we describe an original method to pre- facilitated by the use within the OBO community of equi- dict novel GO annotations for genes associated with valence axioms and logical definitions and by curated alleles that have existing MP annotations. We apply our inter-ontology links. Semantic similarity measures based derived set of rules to a set of papers
Load more

Recommended publications
  • (UPCR) Levels in Diabetic Nephropathy Patients
    Journal of Personalized Medicine Article Gender Differences in Genetic Associations of RAB38 with Urinary Protein-to-Creatinine Ratio (UPCR) Levels in Diabetic Nephropathy Patients 1, 2,3,4, 1 1 Zhi-Lei Yu y, Chung-Shun Wong y , Yi Ting Lai , Wan-Hsuan Chou , Imaniar Noor Faridah 1,5, Chih-Chin Kao 6,7 , Yuh-Feng Lin 2,7,8,* and Wei-Chiao Chang 1,9,10,11,* 1 Department of Clinical Pharmacy, School of Pharmacy, Taipei Medical University, Taipei 110301, Taiwan; [email protected] (Z.-L.Y.); [email protected] (Y.T.L.); [email protected] (W.-H.C.); [email protected] (I.N.F.) 2 Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan; [email protected] 3 Department of Emergency Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City 235041, Taiwan 4 Department of Emergency Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan 5 Faculty of Pharmacy, Ahmad Dahlan University, Yogyakarta 55164, Indonesia 6 Division of Nephrology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 110301, Taiwan; [email protected] 7 Division of Nephrology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei 110301, Taiwan 8 Division of Nephrology, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan 9 Master Program for Clinical Pharmacogenomics and Pharmacoproteomics, School of Pharmacy, Taipei Medical University, Taipei 110301, Taiwan 10 Integrative Research Center for Critical Care, Wan Fang Hospital, Taipei Medical University, Taipei 110301, Taiwan 11 Department of Medical Research, Shuang Ho Hospital, Taipei Medical University, New Taipei City 235041, Taiwan * Correspondence: [email protected] (Y.-F.L.); [email protected] (W.-C.C.) Z.-L.Y.
    [Show full text]
  • Computational Genome-Wide Identification of Heat Shock Protein Genes in the Bovine Genome [Version 1; Peer Review: 2 Approved, 1 Approved with Reservations]
    F1000Research 2018, 7:1504 Last updated: 08 AUG 2021 RESEARCH ARTICLE Computational genome-wide identification of heat shock protein genes in the bovine genome [version 1; peer review: 2 approved, 1 approved with reservations] Oyeyemi O. Ajayi1,2, Sunday O. Peters3, Marcos De Donato2,4, Sunday O. Sowande5, Fidalis D.N. Mujibi6, Olanrewaju B. Morenikeji2,7, Bolaji N. Thomas 8, Matthew A. Adeleke 9, Ikhide G. Imumorin2,10,11 1Department of Animal Breeding and Genetics, Federal University of Agriculture, Abeokuta, Nigeria 2International Programs, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, 14853, USA 3Department of Animal Science, Berry College, Mount Berry, GA, 30149, USA 4Departamento Regional de Bioingenierias, Tecnologico de Monterrey, Escuela de Ingenieria y Ciencias, Queretaro, Mexico 5Department of Animal Production and Health, Federal University of Agriculture, Abeokuta, Nigeria 6Usomi Limited, Nairobi, Kenya 7Department of Animal Production and Health, Federal University of Technology, Akure, Nigeria 8Department of Biomedical Sciences, Rochester Institute of Technology, Rochester, NY, 14623, USA 9School of Life Sciences, University of KwaZulu-Natal, Durban, 4000, South Africa 10School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, 30032, USA 11African Institute of Bioscience Research and Training, Ibadan, Nigeria v1 First published: 20 Sep 2018, 7:1504 Open Peer Review https://doi.org/10.12688/f1000research.16058.1 Latest published: 20 Sep 2018, 7:1504 https://doi.org/10.12688/f1000research.16058.1 Reviewer Status Invited Reviewers Abstract Background: Heat shock proteins (HSPs) are molecular chaperones 1 2 3 known to bind and sequester client proteins under stress. Methods: To identify and better understand some of these proteins, version 1 we carried out a computational genome-wide survey of the bovine 20 Sep 2018 report report report genome.
    [Show full text]
  • Architecture of a Lymphomyeloid Developmental Switch Controlled by PU.1, Notch and Gata3 Marissa Morales Del Real and Ellen V
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Caltech Authors DEVELOPMENT AND STEM CELLS RESEARCH ARTICLE 1207 Development 140, 1207-1219 (2013) doi:10.1242/dev.088559 © 2013. Published by The Company of Biologists Ltd Architecture of a lymphomyeloid developmental switch controlled by PU.1, Notch and Gata3 Marissa Morales Del Real and Ellen V. Rothenberg* SUMMARY Hematopoiesis is a classic system with which to study developmental potentials and to investigate gene regulatory networks that control choices among alternate lineages. T-cell progenitors seeding the thymus retain several lineage potentials. The transcription factor PU.1 is involved in the decision to become a T cell or a myeloid cell, and the developmental outcome of expressing PU.1 is dependent on exposure to Notch signaling. PU.1-expressing T-cell progenitors without Notch signaling often adopt a myeloid program, whereas those exposed to Notch signals remain in a T-lineage pathway. Here, we show that Notch signaling does not alter PU.1 transcriptional activity by degradation/alteration of PU.1 protein. Instead, Notch signaling protects against the downregulation of T-cell factors so that a T-cell transcriptional network is maintained. Using an early T-cell line, we describe two branches of this network. The first involves inhibition of E-proteins by PU.1 and the resulting inhibition of Notch signaling target genes. Effects of E- protein inhibition can be reversed by exposure to Notch signaling. The second network is dependent on the ability of PU.1 to inhibit important T-cell transcription factor genes such as Myb, Tcf7 and Gata3 in the absence of Notch signaling.
    [Show full text]
  • Supplementary Figure 1. Qrt-PCR Analyses for Measuring the Knockdown Efficiency of Transcription Factors. A549 and U937 Cells Kn
    Supplementary Figure 1. qRT-PCR analyses for measuring the knockdown efficiency of transcription factors. A549 and U937 cells knocking down or overexpressing different transcription factors including NF-κB subunits p65 and p50 (A), HIF1 (B), TCF4 (C), AP-1 subunits c-Jun and c-FOS (D), or FOXP3 (E) were subjected to RNA isolation, followed by qRT-PCR analyses to examine the expression of these transcription factors. **P<0.01 and ***P<0.001. 1 Supplementary Figure 2. Effects of FOXP3 downregulation and overexpression on the expression of NLRP1, IL1B and IL18. A549 and U937 cells were transfected with si-FOXP3 or pCDNA3-2×Flag-FOXP3. After 24 h, cells were subjected to RNA isolation, followed by qRT-PCR analyses to examine the expression of NLRP1 (A), IL1B (B) and IL18 (C). ***P<0.001. 2 Supplementary Figure 3. Effects of CtBP2 downregulation and overexpression on the expression of miR-199a-3p, NLRP1, IL1B and IL18. A549 and U937 cells were transfected with si-CtBP2 or pCDNA3-2×Flag-CtBP2. After 24 h, cells were subjected to RNA isolation, followed by qRT-PCR analyses to examine the expression of miR-199a-3p (A), NLRP1 (B), IL1B (C) and IL18 (D). ***P<0.001. 3 Supplementary Figure 4. CHFTC specifically bond to the promoter of miR-199a-3p. (A and B) Effects of CtBP2 knockdown and overexpression on HDAC1 and FOXP3 protein levels. A549 cells were transfected with two CtBP2-specific shRNAs and pCDNA3-2×Flag-CtBP2 to obtain two CtBP2-knockdown cell lines (KD-1 and KD-2) (A) and two CtBP2-overexpression cells lines (OE-1 and OE-2) (B), respectively.
    [Show full text]
  • Gene Regulation and Speciation in House Mice
    Downloaded from genome.cshlp.org on September 26, 2021 - Published by Cold Spring Harbor Laboratory Press Research Gene regulation and speciation in house mice Katya L. Mack,1 Polly Campbell,2 and Michael W. Nachman1 1Museum of Vertebrate Zoology and Department of Integrative Biology, University of California, Berkeley, California 94720-3160, USA; 2Department of Integrative Biology, Oklahoma State University, Stillwater, Oklahoma 74078, USA One approach to understanding the process of speciation is to characterize the genetic architecture of post-zygotic isolation. As gene regulation requires interactions between loci, negative epistatic interactions between divergent regulatory elements might underlie hybrid incompatibilities and contribute to reproductive isolation. Here, we take advantage of a cross between house mouse subspecies, where hybrid dysfunction is largely unidirectional, to test several key predictions about regulatory divergence and reproductive isolation. Regulatory divergence between Mus musculus musculus and M. m. domesticus was charac- terized by studying allele-specific expression in fertile hybrid males using mRNA-sequencing of whole testes. We found ex- tensive regulatory divergence between M. m. musculus and M. m. domesticus, largely attributable to cis-regulatory changes. When both cis and trans changes occurred, they were observed in opposition much more often than expected under a neutral model, providing strong evidence of widespread compensatory evolution. We also found evidence for lineage-specific positive se- lection on a subset of genes related to transcriptional regulation. Comparisons of fertile and sterile hybrid males identified a set of genes that were uniquely misexpressed in sterile individuals. Lastly, we discovered a nonrandom association between these genes and genes showing evidence of compensatory evolution, consistent with the idea that regulatory interactions might contribute to Dobzhansky-Muller incompatibilities and be important in speciation.
    [Show full text]
  • Transcriptome Analyses of Rhesus Monkey Pre-Implantation Embryos Reveal A
    Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press Transcriptome analyses of rhesus monkey pre-implantation embryos reveal a reduced capacity for DNA double strand break (DSB) repair in primate oocytes and early embryos Xinyi Wang 1,3,4,5*, Denghui Liu 2,4*, Dajian He 1,3,4,5, Shengbao Suo 2,4, Xian Xia 2,4, Xiechao He1,3,6, Jing-Dong J. Han2#, Ping Zheng1,3,6# Running title: reduced DNA DSB repair in monkey early embryos Affiliations: 1 State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China 2 Key Laboratory of Computational Biology, CAS Center for Excellence in Molecular Cell Science, Collaborative Innovation Center for Genetics and Developmental Biology, Chinese Academy of Sciences-Max Planck Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China 3 Yunnan Key Laboratory of Animal Reproduction, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan 650223, China 4 University of Chinese Academy of Sciences, Beijing, China 5 Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, Yunnan 650204, China 6 Primate Research Center, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China * Xinyi Wang and Denghui Liu contributed equally to this work 1 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press # Correspondence: Jing-Dong J. Han, Email: [email protected]; Ping Zheng, Email: [email protected] Key words: rhesus monkey, pre-implantation embryo, DNA damage 2 Downloaded from genome.cshlp.org on September 23, 2021 - Published by Cold Spring Harbor Laboratory Press ABSTRACT Pre-implantation embryogenesis encompasses several critical events including genome reprogramming, zygotic genome activation (ZGA) and cell fate commitment.
    [Show full text]
  • A Network of Bhlhzip Transcription Factors in Melanoma: Interactions of MITF, TFEB and TFE3
    A network of bHLHZip transcription factors in melanoma: Interactions of MITF, TFEB and TFE3 Josué A. Ballesteros Álvarez Thesis for the degree of Philosophiae Doctor January 2019 Net bHLHZip umritunarþátta í sortuæxlum: Samstarf milli MITF, TFEB og TFE3 Josué A. Ballesteros Álvarez Ritgerð til doktorsgráðu Leiðbeinandi/leiðbeinendur: Eiríkur Steingrímsson Doktorsnefnd: Margrét H. Ögmundsdóttir Þórarinn Guðjónsson Jórunn E. Eyfjörð Lars Rönnstrand Janúar 2019 Thesis for a doctoral degree at tHe University of Iceland. All rigHts reserved. No Part of tHis Publication may be reProduced in any form witHout tHe Prior permission of the copyright holder. © Josue A. Ballesteros Álvarez. 2019 ISBN 978-9935-9421-4-2 Printing by HáskólaPrent Reykjavik, Iceland 2019 Ágrip StjórnPróteinin MITF , TFEB, TFE3 og TFEC (stundum nefnd MiT-TFE þættirnir) tilheyra bHLHZip fjölskyldu umritunarþátta sem bindast DNA og stjórna tjáningu gena. MITF er mikilvægt fyrir myndun og starfsemi litfruma en ættingjar þess, TFEB og TFE3, stjórna myndun og starfsemi lysósóma og sjálfsáti. Sjálfsát er líffræðilegt ferli sem gegnir mikilvægu hlutverki í starfsemi fruma en getur einnig haft áHrif á myndun og meðHöndlun sjúkdóma. Í verkefni þessu var samstarf MITF, TFE3 og TFEB Próteinanna skoðað í sortuæxlisfrumum og hvaða áhrif þau Hafa á tjáningu hvers annars. Eins og MITF eru TFEB og TFE3 genin tjáð í sortuæxlisfrumum og sortuæxlum; TFEC er ekki tjáð í þessum frumum og var því ekki skoðað í þessu verkefni. Með notkun sérvirkra hindra var sýnt að boðleiðir hafa áhrif á staðsetningu próteinanna þriggja í sortuæxlisfrumum. Umritunarþættir þessir geta bundist skyldum DNA-bindisetum og haft áhrif á tjáningu gena sem eru nauðsynleg fyrir myndun bæði lýsósóma og melanósóma.
    [Show full text]
  • Exploring Prostate Cancer Genome Reveals Simultaneous Losses of PTEN, FAS and PAPSS2 in Patients with PSA Recurrence After Radical Prostatectomy
    Int. J. Mol. Sci. 2015, 16, 3856-3869; doi:10.3390/ijms16023856 OPEN ACCESS International Journal of Molecular Sciences ISSN 1422-0067 www.mdpi.com/journal/ijms Article Exploring Prostate Cancer Genome Reveals Simultaneous Losses of PTEN, FAS and PAPSS2 in Patients with PSA Recurrence after Radical Prostatectomy Chinyere Ibeawuchi 1, Hartmut Schmidt 2, Reinhard Voss 3, Ulf Titze 4, Mahmoud Abbas 5, Joerg Neumann 6, Elke Eltze 7, Agnes Marije Hoogland 8, Guido Jenster 9, Burkhard Brandt 10 and Axel Semjonow 1,* 1 Prostate Center, Department of Urology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebaeude 1A, Muenster D-48149, Germany; E-Mail: [email protected] 2 Center for Laboratory Medicine, University Hospital Muenster, Albert-Schweitzer-Campus 1, Gebaeude 1A, Muenster D-48149, Germany; E-Mail: [email protected] 3 Interdisciplinary Center for Clinical Research, University of Muenster, Albert-Schweitzer-Campus 1, Gebaeude D3, Domagkstrasse 3, Muenster D-48149, Germany; E-Mail: [email protected] 4 Pathology, Lippe Hospital Detmold, Röntgenstrasse 18, Detmold D-32756, Germany; E-Mail: [email protected] 5 Institute of Pathology, Mathias-Spital-Rheine, Frankenburg Street 31, Rheine D-48431, Germany; E-Mail: [email protected] 6 Institute of Pathology, Klinikum Osnabrueck, Am Finkenhuegel 1, Osnabrueck D-49076, Germany; E-Mail: [email protected] 7 Institute of Pathology, Saarbrücken-Rastpfuhl, Rheinstrasse 2, Saarbrücken D-66113, Germany; E-Mail: [email protected] 8 Department
    [Show full text]
  • Multiple Cellular Proteins Interact with LEDGF/P75 Through a Conserved Unstructured Consensus Motif
    ARTICLE Received 19 Jan 2015 | Accepted 1 Jul 2015 | Published 6 Aug 2015 DOI: 10.1038/ncomms8968 Multiple cellular proteins interact with LEDGF/p75 through a conserved unstructured consensus motif Petr Tesina1,2,3,*, Katerˇina Cˇerma´kova´4,*, Magdalena Horˇejsˇ´ı3, Katerˇina Procha´zkova´1, Milan Fa´bry3, Subhalakshmi Sharma4, Frauke Christ4, Jonas Demeulemeester4, Zeger Debyser4, Jan De Rijck4,**, Va´clav Veverka1,** & Pavlı´na Rˇeza´cˇova´1,3,** Lens epithelium-derived growth factor (LEDGF/p75) is an epigenetic reader and attractive therapeutic target involved in HIV integration and the development of mixed lineage leukaemia (MLL1) fusion-driven leukaemia. Besides HIV integrase and the MLL1-menin complex, LEDGF/p75 interacts with various cellular proteins via its integrase binding domain (IBD). Here we present structural characterization of IBD interactions with transcriptional repressor JPO2 and domesticated transposase PogZ, and show that the PogZ interaction is nearly identical to the interaction of LEDGF/p75 with MLL1. The interaction with the IBD is maintained by an intrinsically disordered IBD-binding motif (IBM) common to all known cellular partners of LEDGF/p75. In addition, based on IBM conservation, we identify and validate IWS1 as a novel LEDGF/p75 interaction partner. Our results also reveal how HIV integrase efficiently displaces cellular binding partners from LEDGF/p75. Finally, the similar binding modes of LEDGF/p75 interaction partners represent a new challenge for the development of selective interaction inhibitors. 1 Institute of Organic Chemistry and Biochemistry of the ASCR, v.v.i., Flemingovo nam. 2, 166 10 Prague, Czech Republic. 2 Department of Genetics and Microbiology, Faculty of Science, Charles University in Prague, Vinicna 5, 128 44 Prague, Czech Republic.
    [Show full text]
  • Taf7l Cooperates with Trf2 to Regulate Spermiogenesis
    Taf7l cooperates with Trf2 to regulate spermiogenesis Haiying Zhoua,b, Ivan Grubisicb,c, Ke Zhengd,e, Ying Heb, P. Jeremy Wangd, Tommy Kaplanf, and Robert Tjiana,b,1 aHoward Hughes Medical Institute and bDepartment of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, California Institute for Regenerative Medicine Center of Excellence, University of California, Berkeley, CA 94720; cUniversity of California Berkeley–University of California San Francisco Graduate Program in Bioengineering, University of California, Berkeley, CA 94720; dDepartment of Animal Biology, University of Pennsylvania School of Veterinary Medicine, Philadelphia, PA 19104; eState Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 210029, People’s Republic of China; and fSchool of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem 91904, Israel Contributed by Robert Tjian, September 11, 2013 (sent for review August 20, 2013) TATA-binding protein (TBP)-associated factor 7l (Taf7l; a paralogue (Taf4b; a homolog of Taf4) (9), TBP-related factor 2 (Trf2) (10, of Taf7) and TBP-related factor 2 (Trf2) are components of the core 11), and Taf7l (12, 13). For example, mice bearing mutant or promoter complex required for gene/tissue-specific transcription deficient CREM showed decreased postmeiotic gene expression of protein-coding genes by RNA polymerase II. Previous studies and defective spermiogenesis (14). Mice deficient in Taf4b, reported that Taf7l knockout (KO) mice exhibit structurally abnor- a testis-specific homolog of Taf4, are initially normal but undergo mal sperm, reduced sperm count, weakened motility, and compro- progressive germ-cell loss and become infertile by 3 mo of age −/Y mised fertility.
    [Show full text]
  • Co-Expression Module Analysis Reveals Biological Processes
    Shi et al. BMC Systems Biology 2010, 4:74 http://www.biomedcentral.com/1752-0509/4/74 RESEARCH ARTICLE Open Access Co-expressionResearch article module analysis reveals biological processes, genomic gain, and regulatory mechanisms associated with breast cancer progression Zhiao Shi1,2, Catherine K Derow3 and Bing Zhang*3 Abstract Background: Gene expression signatures are typically identified by correlating gene expression patterns to a disease phenotype of interest. However, individual gene-based signatures usually suffer from low reproducibility and interpretability. Results: We have developed a novel algorithm Iterative Clique Enumeration (ICE) for identifying relatively independent maximal cliques as co-expression modules and a module-based approach to the analysis of gene expression data. Applying this approach on a public breast cancer dataset identified 19 modules whose expression levels were significantly correlated with tumor grade. The correlations were reproducible for 17 modules in an independent breast cancer dataset, and the reproducibility was considerably higher than that based on individual genes or modules identified by other algorithms. Sixteen out of the 17 modules showed significant enrichment in certain Gene Ontology (GO) categories. Specifically, modules related to cell proliferation and immune response were up-regulated in high- grade tumors while those related to cell adhesion was down-regulated. Further analyses showed that transcription factors NYFB, E2F1/E2F3, NRF1, and ELK1 were responsible for the up-regulation of the cell proliferation modules. IRF family and ETS family proteins were responsible for the up-regulation of the immune response modules. Moreover, inhibition of the PPARA signaling pathway may also play an important role in tumor progression.
    [Show full text]
  • Transcriptomic Signature and Metabolic Programming of Bovine Classical and Nonclassical Monocytes Indicate Distinct Functional Specializations
    bioRxiv preprint doi: https://doi.org/10.1101/2020.10.30.362731; this version posted November 1, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Transcriptomic signature and metabolic programming of bovine classical and nonclassical monocytes indicate distinct functional specializations Stephanie C. Talker1,2, G. Tuba Barut1,2, Reto Rufener3, Lilly von Münchow4, Artur Summerfield1,2 1Institute of Virology and Immunology, Bern and Mittelhäusern, Switzerland 2Department of Infectious Diseases and Pathobiology, Vetsuisse Faculty, University of Bern, Bern, Switzerland 3Institute of Parasitology, Vetsuisse Faculty, University of Bern, Bern, Switzerland 4 Bucher Biotec AG, Basel, Switzerland *Correspondence: Corresponding Author [email protected] Keywords: monocyte subsets, transcriptome, metabolism, cattle Abstract Similar to human monocytes, bovine monocytes can be split into CD14+CD16- classical and CD14-CD16+ nonclassical monocytes (cM and ncM, respectively). Here, we present an in-depth analysis of their steady-state transcriptomes, highlighting pronounced functional specializations. Gene transcription indicates that pro-inflammatory and antibacterial processes are associated with cM, while ncM appear to be specialized in regulatory/anti-inflammatory functions and tissue repair, as well as antiviral responses and T-cell immunomodulation. In support of these functional differences, we found that oxidative phosphorylation prevails in ncM, whereas cM are clearly biased towards aerobic glycolysis. Furthermore, bovine monocyte subsets differed in their responsiveness to TLR ligands, supporting an antiviral role of ncM. Taken together, these data clearly indicate a variety of subset-specific functions in cM and ncM that are likely to be transferable to monocyte subsets of other species, including humans.
    [Show full text]